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Jacob Moss

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3 papers

2 author rows

TMLR Journal 2024 Journal Article

Deep Kernel Learning of Nonlinear Latent Force Models

Jacob Moss
Jeremy England
Pietro Lio

Scientific processes are often modelled by sets of differential equations. As datasets grow, individually fitting these models and quantifying their uncertainties becomes a computationally challenging task. Latent force models offer a mathematically-grounded balance between data-driven and mechanistic inference in such dynamical systems, whilst accounting for stochasticity in observations and parameters. However, the required derivation and computation of the posterior kernel terms over a low-dimensional latent force is rarely tractable, requiring approximations for complex scenarios such as nonlinear dynamics. In this paper, we overcome this issue by posing the problem as learning the solution operator itself to a class of latent force models, thereby improving the scalability of these models. This is achieved by employing a deep kernel along with a meta-learned embedding of the output functions. Finally, we demonstrate the ability to extrapolate a solution operator trained on simulations to real experimental datasets, as well as scaling to large datasets.

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NeurIPS Conference 2023 Conference Paper

GAUCHE: A Library for Gaussian Processes in Chemistry

Ryan-Rhys Griffiths
Leo Klarner
Henry Moss
Aditya Ravuri
Sang Truong
Yuanqi Du
Samuel Stanton
Gary Tom

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry. Gaussian processes have long been a cornerstone of probabilistic machine learning, affording particular advantages for uncertainty quantification and Bayesian optimisation. Extending Gaussian processes to molecular representations, however, necessitates kernels defined over structured inputs such as graphs, strings and bit vectors. By providing such kernels in a modular, robust and easy-to-use framework, we seek to enable expert chemists and materials scientists to make use of state-of-the-art black-box optimization techniques. Motivated by scenarios frequently encountered in practice, we showcase applications for GAUCHE in molecular discovery, chemical reaction optimisation and protein design. The codebase is made available at https: //github. com/leojklarner/gauche.

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ICLR Conference 2021 Conference Paper

Neural ODE Processes

Alexander Luke Ian Norcliffe
Cristian Bodnar
Ben J. Day
Jacob Moss
Pietro Liò

Neural Ordinary Differential Equations (NODEs) use a neural network to model the instantaneous rate of change in the state of a system. However, despite their apparent suitability for dynamics-governed time-series, NODEs present a few disadvantages. First, they are unable to adapt to incoming data-points, a fundamental requirement for real-time applications imposed by the natural direction of time. Second, time-series are often composed of a sparse set of measurements that could be explained by many possible underlying dynamics. NODEs do not capture this uncertainty. In contrast, Neural Processes (NPs) are a new class of stochastic processes providing uncertainty estimation and fast data-adaptation, but lack an explicit treatment of the flow of time. To address these problems, we introduce Neural ODE Processes (NDPs), a new class of stochastic processes determined by a distribution over Neural ODEs. By maintaining an adaptive data-dependent distribution over the underlying ODE, we show that our model can successfully capture the dynamics of low-dimensional systems from just a few data-points. At the same time, we demonstrate that NDPs scale up to challenging high-dimensional time-series with unknown latent dynamics such as rotating MNIST digits.

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